A novel data dissemination model for organic data flows

The number of computing devices of the IoT are expected to grow exponentially. To address the communication needs of the IoT, research is being done to develop new networking architectures and to extend existing architectures. An area that lacks attention in these efforts is the emphasis on utilisat...

Full description

Autores:
Foerster, Anna
Udugama, Asanga
Görg, Carmelita
Kuladinithi, Koojana
Timm-Giel, Andreas
Cama Pinto, Alejandro
Tipo de recurso:
Article of journal
Fecha de publicación:
2015
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/974
Acceso en línea:
https://hdl.handle.net/11323/974
https://repositorio.cuc.edu.co/
Palabra clave:
Internet of things
Opportunistic networks
Organic data flows
Reinforcement algorithms
Rights
openAccess
License
Atribución – No comercial – Compartir igual
id RCUC2_0b416b8a57f96fc10d438d21bd5f5149
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repository_id_str
dc.title.eng.fl_str_mv A novel data dissemination model for organic data flows
title A novel data dissemination model for organic data flows
spellingShingle A novel data dissemination model for organic data flows
Internet of things
Opportunistic networks
Organic data flows
Reinforcement algorithms
title_short A novel data dissemination model for organic data flows
title_full A novel data dissemination model for organic data flows
title_fullStr A novel data dissemination model for organic data flows
title_full_unstemmed A novel data dissemination model for organic data flows
title_sort A novel data dissemination model for organic data flows
dc.creator.fl_str_mv Foerster, Anna
Udugama, Asanga
Görg, Carmelita
Kuladinithi, Koojana
Timm-Giel, Andreas
Cama Pinto, Alejandro
dc.contributor.author.spa.fl_str_mv Foerster, Anna
Udugama, Asanga
Görg, Carmelita
Kuladinithi, Koojana
Timm-Giel, Andreas
Cama Pinto, Alejandro
dc.subject.eng.fl_str_mv Internet of things
Opportunistic networks
Organic data flows
Reinforcement algorithms
topic Internet of things
Opportunistic networks
Organic data flows
Reinforcement algorithms
description The number of computing devices of the IoT are expected to grow exponentially. To address the communication needs of the IoT, research is being done to develop new networking architectures and to extend existing architectures. An area that lacks attention in these efforts is the emphasis on utilisation of omnipresent local data. There are a number of issues (e.g., underutilisation of local resources and dependence on cloud based data) that need to be addressed to exploit the benefits of utilising local data. We present a novel data dissemination model, called the Organic Data Dissemination (ODD) model to utilise the omni-present data around us, where devices deployed with the ODD model are able to operate even without the existence of networking infrastructure. The realisation of the ODD model requires innovations in many different area including the areas of opportunistic communications, naming of information, direct peer-to-peer communications and reinforcement learning. This paper focuses on highlighting the usage of the ODD model in real application scenarios and the details of the architectural components.
publishDate 2015
dc.date.issued.none.fl_str_mv 2015
dc.date.accessioned.none.fl_str_mv 2018-11-14T16:59:34Z
dc.date.available.none.fl_str_mv 2018-11-14T16:59:34Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.issn.spa.fl_str_mv 1867-8211
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/974
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 1867-8211
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/974
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.spa.fl_str_mv Atribución – No comercial – Compartir igual
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Atribución – No comercial – Compartir igual
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.spa.fl_str_mv Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
institution Corporación Universidad de la Costa
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spelling Foerster, AnnaUdugama, AsangaGörg, CarmelitaKuladinithi, KoojanaTimm-Giel, AndreasCama Pinto, Alejandro2018-11-14T16:59:34Z2018-11-14T16:59:34Z20151867-8211https://hdl.handle.net/11323/974Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The number of computing devices of the IoT are expected to grow exponentially. To address the communication needs of the IoT, research is being done to develop new networking architectures and to extend existing architectures. An area that lacks attention in these efforts is the emphasis on utilisation of omnipresent local data. There are a number of issues (e.g., underutilisation of local resources and dependence on cloud based data) that need to be addressed to exploit the benefits of utilising local data. We present a novel data dissemination model, called the Organic Data Dissemination (ODD) model to utilise the omni-present data around us, where devices deployed with the ODD model are able to operate even without the existence of networking infrastructure. The realisation of the ODD model requires innovations in many different area including the areas of opportunistic communications, naming of information, direct peer-to-peer communications and reinforcement learning. 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